Crexi Property Listings Scraper

Pull commercial real estate listings sourced from Crexi as flat, ready-to-use rows. Filter across 230,000+ US commercial properties by state, city, ZIP, county, sale status, price, building size, lot size, and construction year. Every row lands as a clean JSON record with the full address, coordinates, asking price, brokerage, cover photo, and a direct Crexi link, so it drops straight into Excel, Google Sheets, SQL, or any BI tool.

No login, cookies, browser, or scraping setup. Set the filters, hit Start.

Why this actor

• Curated CRE column names, nothing that looks like a raw HTML dump
• Auction, call-for-offers, sold, and off-market listings, not just for-sale
• Flat records with the fields CRE analysts actually want (asking price, sqft, lot, broker, brokerage, listed date)
• Two hard caps (maxItems, requestTimeoutSecs) so runs stay predictable
• Works out of the box

Input parameters

Everything is optional and freely combinable. Leave the whole form empty to search the entire US.

Field	Type	Default	Description
stateCode	string	CA	Two-letter US state code (CA, TX, NY).
state	string		Full state name (California, Texas).
city	string		City name. Matches across all states unless combined with stateCode.
zipCode	string		Five-digit ZIP code.
county	string		County name, e.g. Stanislaus County.
countyCode	string		County FIPS code, e.g. 6037.
address	string		Street address to match.
saleStatus	select	for_sale	One of for_sale, call_for_offers, auction, sold, off_market.
sortBy	select		newest_listed, oldest_listed, price_high_to_low, price_low_to_high, or recently_updated.
minAskingPrice / maxAskingPrice	integer		Asking price range in USD, inclusive.
minBuildingSqft / maxBuildingSqft	integer		Building area range in square feet.
minLotSqft / maxLotSqft	integer		Lot size range in square feet (1 acre = 43,560 sqft).
minYearBuilt / maxYearBuilt	integer		Construction year range.
startPage	integer	1	Result page to start from (up to 200 listings per page).
maxItems	integer	100	Stop after this many listings (fetched in pages of 200). Max 1000.
requestTimeoutSecs	integer	30	Timeout per API request.

Example inputs

1. Retail centers under $2M in Los Angeles County

{
    "state": "California",
    "county": "Los Angeles County",
    "saleStatus": "for_sale",
    "maxAskingPrice": 2000000,
    "sortBy": "price_low_to_high",
    "maxItems": 200
}

2. Large warehouses in Texas (25k+ sqft), newest first

{
    "stateCode": "TX",
    "saleStatus": "for_sale",
    "minBuildingSqft": 25000,
    "sortBy": "newest_listed",
    "maxItems": 500
}

3. Every active auction in Florida

{
    "stateCode": "FL",
    "saleStatus": "auction",
    "sortBy": "recently_updated",
    "maxItems": 300
}

4. Post-2000 commercial buildings in ZIP 33101 (Miami)

{
    "zipCode": "33101",
    "saleStatus": "for_sale",
    "minYearBuilt": 2000,
    "maxItems": 100
}

5. Recently sold comps in a single county (FIPS 6037)

{
    "countyCode": "6037",
    "saleStatus": "sold",
    "sortBy": "newest_listed",
    "maxItems": 200
}

6. Nationwide sweep of premium listings ($10M+)

{
    "saleStatus": "for_sale",
    "minAskingPrice": 10000000,
    "sortBy": "price_high_to_low",
    "maxItems": 1000
}

7. Land plays: 5+ acre lots in Arizona

{
    "stateCode": "AZ",
    "saleStatus": "for_sale",
    "minLotSqft": 217800,
    "sortBy": "newest_listed",
    "maxItems": 250
}

8. Paginating beyond 1,000 rows

Run A collects listings 1 to 1000, Run B picks up at listing 1001 by setting startPage: 6 (page 6 with a page size of 200 equals rows 1001 to 1200).

{
    "stateCode": "NY",
    "saleStatus": "for_sale",
    "sortBy": "newest_listed",
    "startPage": 6,
    "maxItems": 1000
}

Output columns

Each listing is one flat row. The main columns:

Column	Example
listing_id	crexi_1000002
sale_status	for_sale
street_address	7101 Porter Rd
city / state / zip_code	Niagara Falls / NY / 14304
county	Niagara County
latitude / longitude	43.108545 / -78.983714
asking_price_usd	695000
building_sqft / lot_size_sqft / year_built	building and lot details where Crexi provides them
property_class / property_subtype	commercial plus the Crexi subtype
broker_name / brokerage_name	listing agent and firm
cover_image_url / primary_photo_url / all_photo_urls	hero photo plus the rest of the gallery
crexi_url	direct link to the listing on crexi.com
listed_on / last_updated_on	listing and update dates
data_source	always Crexi for this actor

Sample output

Here is how a run looks in the Apify dataset preview:

And the same record as raw JSON:

{
    "listing_id": "crexi_1000002",
    "sale_status": "for_sale",
    "street_address": "7101 Porter Rd",
    "city": "Niagara Falls",
    "state": "NY",
    "zip_code": "14304",
    "county": "Niagara County",
    "latitude": 43.108545,
    "longitude": -78.983714,
    "asking_price_usd": 695000.0,
    "building_sqft": 12800,
    "lot_size_sqft": 43560,
    "year_built": 1978,
    "property_class": "commercial",
    "property_subtype": "Retail",
    "broker_name": "Ellicott Development",
    "brokerage_name": "Ellicott Development Co.",
    "cover_image_url": "https://images.crexi.com/assets/1000002/...jpg",
    "crexi_url": "https://www.crexi.com/properties/1000002/new-york-7101-porter-rd",
    "listed_on": "2022-11-30",
    "last_updated_on": "2024-05-18",
    "data_source": "Crexi"
}

Use cases

• Deal sourcing: pull every commercial listing in a target county or ZIP, sort newest first, and pipe it into your acquisition workflow
• Market analysis: track asking prices and inventory in a metro area over time by scheduling weekly runs
• Brokerage prospecting: collect broker_name and brokerage_name across a region to build outreach lists
• Auction & CFO tracking: filter by auction or call_for_offers status to catch non-standard sale processes early
• Comp research: filter sale_status: sold with a tight geography to pull recent comps
• Data enrichment: join records to your own property database by address, ZIP, or coordinates

Notes

• Commercial listings do not carry residential fields like beds/baths, so those arrive as null on purpose.
• startPage + maxItems is how you page past the 1,000-row-per-run ceiling.
• The data_source column is always Crexi for this actor; a future variant of this scraper may cover other sources and use the same column to distinguish them.

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